Machine Learning for Evolution Strategies

Kramer, Oliver

ISBN 10: 331933381X / ISBN 13: 9783319333816

Verlag: Springer International Publishing AG, 2016

Series: Studies in Big Data. Num Pages: 133 pages, 38 colour illustrations, biography. BIC Classification: UYQM. Category: (G) General (US: Trade). Dimension: 235 x 155 x 10. Weight in Grams: 374. . 2016. Hardback. . . . . Bestandsnummer des Verkäufers V9783319333816

Inhaltsangabe:

This bookintroduces numerous algorithmic hybridizations between both worlds that showhow machine learning can improve and support evolution strategies. The set ofmethods comprises covariance matrix estimation, meta-modeling of fitness andconstraint functions, dimensionality reduction for search and visualization ofhigh-dimensional optimization processes, and clustering-based niching. Aftergiving an introduction to evolution strategies and machine learning, the bookbuilds the bridge between both worlds with an algorithmic and experimentalperspective. Experiments mostly employ a (1+1)-ES and are implemented in Pythonusing the machine learning library scikit-learn. The examples are conducted ontypical benchmark problems illustrating algorithmic concepts and theirexperimental behavior. The book closes with a discussion of related lines ofresearch.

Von der hinteren Coverseite:

This bookintroduces numerous algorithmic hybridizations between both worlds that showhow machine learning can improve and support evolution strategies. The set ofmethods comprises covariance matrix estimation, meta-modeling of fitness andconstraint functions, dimensionality reduction for search and visualization ofhigh-dimensional optimization processes, and clustering-based niching. Aftergiving an introduction to evolution strategies and machine learning, the bookbuilds the bridge between both worlds with an algorithmic and experimentalperspective. Experiments mostly employ a (1+1)-ES and are implemented in Pythonusing the machine learning library scikit-learn. The examples are conducted ontypical benchmark problems illustrating algorithmic concepts and theirexperimental behavior. The book closes with a discussion of related lines ofresearch.

Bibliografische Details

Titel: Machine Learning for Evolution Strategies
Verlag: Springer International Publishing AG
Erscheinungsdatum: 2016
Einband: Hardcover
Zustand: New